5 Main figures in the paper
- We firstly provide estimations and figures used in the main text.
- These chunks are copied and pasted from subsequent outcome-based result sections.
- Actual graphs and tables in the paper are generated and saved in the subsequent chunks, not the chunks in this section. But they are identical.
5.1 WLS, with trends, Figure 4 (a) & TableC.3 (1)
- Y=UI benefit(total), without covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_total,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.203
## (1.317)
## treat_var:date_2018_03 0.157
## (2.086)
## treat_var:date_2018_04 0.041
## (3.044)
## treat_var:date_2018_05 -0.152
## (3.946)
## treat_var:date_2018_06 2.732
## (3.987)
## treat_var:date_2018_07 -0.796
## (4.582)
## treat_var:date_2018_08 -3.057
## (5.653)
## treat_var:date_2018_09 -5.392
## (7.173)
## treat_var:date_2018_10 -8.169
## (8.049)
## treat_var:date_2018_11 -5.202
## (6.606)
## treat_var:date_2018_12 -10.366
## (7.818)
## treat_var:date_2019_01 -8.453
## (7.082)
## treat_var:date_2019_02 -3.479
## (6.402)
## treat_var:date_2019_03 -4.628
## (7.016)
## treat_var:date_2019_04 -9.847
## (8.487)
## treat_var:date_2019_05 -0.802
## (7.182)
## treat_var:date_2019_06 -11.318
## (8.782)
## treat_var:date_2019_07 -7.778
## (8.555)
## treat_var:date_2019_08 -0.708
## (5.280)
## treat_var:date_2019_09 -0.297
## (5.676)
## treat_var:date_2019_10 -0.524
## (5.351)
## treat_var:date_2019_11 -6.370 *
## (2.724)
## treat_var:date_2019_12 -2.146
## (2.064)
## treat_var:date_2020_02 -3.719
## (3.847)
## treat_var:date_2020_03 0.230
## (5.526)
## treat_var:date_2020_04 4.335
## (6.428)
## treat_var:date_2020_05 1.688
## (8.533)
## treat_var:date_2020_06 8.374
## (12.297)
## treat_var:date_2020_07 11.007
## (11.834)
## treat_var:date_2020_08 5.319
## (14.496)
## treat_var:date_2020_09 9.438
## (15.094)
## as.factor(id)1:year_month_id -0.527
## (0.505)
## as.factor(id)2:year_month_id 0.233
## (0.297)
## as.factor(id)3:year_month_id -0.383
## (0.335)
## as.factor(id)4:year_month_id 1.741 ***
## (0.394)
## as.factor(id)5:year_month_id 0.380
## (0.380)
## as.factor(id)6:year_month_id 0.744 *
## (0.361)
## as.factor(id)7:year_month_id 2.437 ***
## (0.342)
## as.factor(id)8:year_month_id 1.548 ***
## (0.211)
## as.factor(id)9:year_month_id 2.577 ***
## (0.222)
## as.factor(id)10:year_month_id 3.907 ***
## (0.161)
## as.factor(id)11:year_month_id 1.298 **
## (0.461)
## as.factor(id)12:year_month_id 0.702
## (0.442)
## as.factor(id)13:year_month_id 1.137 *
## (0.485)
## as.factor(id)14:year_month_id 0.353
## (0.611)
## as.factor(id)15:year_month_id 0.326
## (0.277)
## as.factor(id)16:year_month_id 0.919 **
## (0.304)
## as.factor(id)17:year_month_id 0.979 ***
## (0.247)
## as.factor(id)18:year_month_id -0.292
## (0.174)
## as.factor(id)19:year_month_id 2.106 ***
## (0.266)
## as.factor(id)20:year_month_id 2.499 ***
## (0.169)
## as.factor(id)21:year_month_id 0.225
## (0.193)
## as.factor(id)22:year_month_id 1.553 ***
## (0.231)
## as.factor(id)23:year_month_id 1.037 ***
## (0.259)
## as.factor(id)24:year_month_id 0.408
## (0.264)
## as.factor(id)25:year_month_id 1.513 ***
## (0.243)
## as.factor(id)26:year_month_id -0.415
## (0.408)
## as.factor(id)27:year_month_id 0.848
## (0.591)
## as.factor(id)28:year_month_id -0.919
## (0.462)
## as.factor(id)29:year_month_id 0.242
## (0.532)
## as.factor(id)30:year_month_id -0.147
## (0.524)
## as.factor(id)31:year_month_id -1.857 ***
## (0.347)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -4.095 ***
## (0.248)
## as.factor(id)34:year_month_id -3.574 ***
## (0.268)
## as.factor(id)35:year_month_id -0.693 *
## (0.297)
## as.factor(id)36:year_month_id -0.683 *
## (0.332)
## as.factor(id)37:year_month_id -1.294 ***
## (0.313)
## as.factor(id)38:year_month_id -2.377 ***
## (0.290)
## as.factor(id)39:year_month_id -1.692 ***
## (0.242)
## as.factor(id)40:year_month_id -0.219
## (0.344)
## as.factor(id)41:year_month_id -1.800 ***
## (0.068)
## as.factor(id)42:year_month_id 0.080
## (0.243)
## as.factor(id)43:year_month_id 3.384 ***
## (0.245)
## as.factor(id)44:year_month_id 0.529
## (0.354)
## as.factor(id)45:year_month_id -0.200
## (0.248)
## as.factor(id)46:year_month_id -1.135 **
## (0.397)
## as.factor(id)47:year_month_id 1.244
## (0.639)
## -------------------------------------------
## R^2 0.876
## Adj. R^2 0.862
## Num. obs. 1551
## RMSE 759.919
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_total_WLS_trend")
# Event study graph
graph_yoy_total_WLS_trend <- event_study_graph(data = df_estimates ,
graph_title = "yoy_total_WLS_trend")
ggplotly(graph_yoy_total_WLS_trend)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
5.2 WLS, with trends, Figure 4 (b) & TableC.4 (1)
- Y=UI benefit(total), with covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_total,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.201
## (1.348)
## treat_var:date_2018_03 0.159
## (2.136)
## treat_var:date_2018_04 0.045
## (3.115)
## treat_var:date_2018_05 -0.146
## (4.039)
## treat_var:date_2018_06 2.738
## (4.077)
## treat_var:date_2018_07 -0.789
## (4.685)
## treat_var:date_2018_08 -3.048
## (5.782)
## treat_var:date_2018_09 -5.381
## (7.337)
## treat_var:date_2018_10 -8.157
## (8.234)
## treat_var:date_2018_11 -5.189
## (6.759)
## treat_var:date_2018_12 -10.352
## (7.995)
## treat_var:date_2019_01 -8.440
## (7.244)
## treat_var:date_2019_02 -3.466
## (6.547)
## treat_var:date_2019_03 -4.614
## (7.177)
## treat_var:date_2019_04 -9.834
## (8.685)
## treat_var:date_2019_05 -0.788
## (7.350)
## treat_var:date_2019_06 -11.303
## (8.988)
## treat_var:date_2019_07 -7.763
## (8.755)
## treat_var:date_2019_08 -0.692
## (5.400)
## treat_var:date_2019_09 -0.280
## (5.805)
## treat_var:date_2019_10 -0.507
## (5.472)
## treat_var:date_2019_11 -6.353 *
## (2.786)
## treat_var:date_2019_12 -2.129
## (2.114)
## treat_var:date_2020_02 12.950
## (8.516)
## treat_var:date_2020_03 21.687
## (12.056)
## treat_var:date_2020_04 20.389
## (14.082)
## treat_var:date_2020_05 33.084 *
## (12.900)
## treat_var:date_2020_06 24.256
## (14.906)
## treat_var:date_2020_07 27.857
## (14.525)
## treat_var:date_2020_08 19.338
## (15.631)
## treat_var:date_2020_09 21.971
## (15.685)
## date_2020_02:google_mobility_index_2020may -0.609
## (0.985)
## date_2020_03:google_mobility_index_2020may -0.316
## (1.221)
## date_2020_04:google_mobility_index_2020may -1.871
## (1.660)
## date_2020_05:google_mobility_index_2020may -2.271
## (1.403)
## date_2020_06:google_mobility_index_2020may -4.525 *
## (1.845)
## date_2020_07:google_mobility_index_2020may -6.403 **
## (2.055)
## date_2020_08:google_mobility_index_2020may -5.421 **
## (1.812)
## date_2020_09:google_mobility_index_2020may -3.440
## (1.947)
## date_2020_02:infection_rate_cumulative2020jun 1.380
## (1.038)
## date_2020_03:infection_rate_cumulative2020jun 2.094
## (1.372)
## date_2020_04:infection_rate_cumulative2020jun 1.117
## (1.497)
## date_2020_05:infection_rate_cumulative2020jun 0.762
## (1.461)
## date_2020_06:infection_rate_cumulative2020jun 2.725
## (2.193)
## date_2020_07:infection_rate_cumulative2020jun 1.290
## (2.020)
## date_2020_08:infection_rate_cumulative2020jun 2.520
## (1.965)
## date_2020_09:infection_rate_cumulative2020jun 3.072
## (1.818)
## date_2020_02:death_rate_cumulative2020jun -12.737
## (12.822)
## date_2020_03:death_rate_cumulative2020jun -22.462
## (16.730)
## date_2020_04:death_rate_cumulative2020jun -13.567
## (18.013)
## date_2020_05:death_rate_cumulative2020jun -7.123
## (16.521)
## date_2020_06:death_rate_cumulative2020jun -29.258
## (27.420)
## date_2020_07:death_rate_cumulative2020jun -14.872
## (24.161)
## date_2020_08:death_rate_cumulative2020jun -24.718
## (21.299)
## date_2020_09:death_rate_cumulative2020jun -30.091
## (18.569)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.005 *
## (0.002)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.007 *
## (0.003)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.008 *
## (0.003)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.010 ***
## (0.003)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.012 **
## (0.004)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.012 ***
## (0.003)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.012 ***
## (0.003)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.010 **
## (0.004)
## date_2020_02:Secondary_industry_ratio 106.694
## (105.929)
## date_2020_03:Secondary_industry_ratio 53.301
## (132.821)
## date_2020_04:Secondary_industry_ratio 32.693
## (170.768)
## date_2020_05:Secondary_industry_ratio -16.396
## (151.768)
## date_2020_06:Secondary_industry_ratio -156.486
## (175.676)
## date_2020_07:Secondary_industry_ratio -7.868
## (183.252)
## date_2020_08:Secondary_industry_ratio 124.040
## (184.816)
## date_2020_09:Secondary_industry_ratio 194.419
## (195.336)
## date_2020_02:Tertiary_industry_ratio -164.182
## (137.360)
## date_2020_03:Tertiary_industry_ratio -245.620
## (191.793)
## date_2020_04:Tertiary_industry_ratio -271.913
## (250.093)
## date_2020_05:Tertiary_industry_ratio -522.607 **
## (189.945)
## date_2020_06:Tertiary_industry_ratio -659.559 *
## (266.320)
## date_2020_07:Tertiary_industry_ratio -648.280 *
## (256.109)
## date_2020_08:Tertiary_industry_ratio -440.917
## (232.385)
## date_2020_09:Tertiary_industry_ratio -313.520
## (213.909)
## date_2020_02:Total_population 0.019
## (0.011)
## date_2020_03:Total_population 0.027
## (0.015)
## date_2020_04:Total_population 0.037
## (0.020)
## date_2020_05:Total_population 0.047 **
## (0.017)
## date_2020_06:Total_population 0.030
## (0.026)
## date_2020_07:Total_population 0.032
## (0.024)
## date_2020_08:Total_population 0.017
## (0.028)
## date_2020_09:Total_population 0.008
## (0.031)
## date_2020_02:Ratio_of_aged_population 0.133
## (0.523)
## date_2020_03:Ratio_of_aged_population -0.165
## (0.605)
## date_2020_04:Ratio_of_aged_population 0.165
## (0.892)
## date_2020_05:Ratio_of_aged_population 0.344
## (0.783)
## date_2020_06:Ratio_of_aged_population -0.183
## (1.043)
## date_2020_07:Ratio_of_aged_population -0.603
## (1.146)
## date_2020_08:Ratio_of_aged_population -1.277
## (1.232)
## date_2020_09:Ratio_of_aged_population -1.629
## (1.284)
## as.factor(id)1:year_month_id 0.026
## (0.401)
## as.factor(id)2:year_month_id 2.110 ***
## (0.197)
## as.factor(id)3:year_month_id 1.013 ***
## (0.196)
## as.factor(id)4:year_month_id 2.747 ***
## (0.323)
## as.factor(id)5:year_month_id 2.256 ***
## (0.375)
## as.factor(id)6:year_month_id 0.996
## (0.574)
## as.factor(id)7:year_month_id 2.437 ***
## (0.470)
## as.factor(id)8:year_month_id 1.689 ***
## (0.294)
## as.factor(id)9:year_month_id 2.366 ***
## (0.354)
## as.factor(id)10:year_month_id 4.475 ***
## (0.445)
## as.factor(id)11:year_month_id 1.430 ***
## (0.329)
## as.factor(id)12:year_month_id 0.994 *
## (0.396)
## as.factor(id)13:year_month_id 1.191 ***
## (0.326)
## as.factor(id)14:year_month_id 1.333 ***
## (0.340)
## as.factor(id)15:year_month_id 1.163 **
## (0.386)
## as.factor(id)16:year_month_id 1.417 *
## (0.615)
## as.factor(id)17:year_month_id 1.548 **
## (0.564)
## as.factor(id)18:year_month_id 0.729
## (0.464)
## as.factor(id)19:year_month_id 2.045 ***
## (0.471)
## as.factor(id)20:year_month_id 2.511 ***
## (0.418)
## as.factor(id)21:year_month_id 0.391
## (0.451)
## as.factor(id)22:year_month_id 1.588 ***
## (0.413)
## as.factor(id)23:year_month_id 0.739 *
## (0.364)
## as.factor(id)24:year_month_id 0.664
## (0.350)
## as.factor(id)25:year_month_id 1.268 *
## (0.476)
## as.factor(id)26:year_month_id -0.170
## (0.345)
## as.factor(id)27:year_month_id 1.503 ***
## (0.370)
## as.factor(id)28:year_month_id -0.340
## (0.388)
## as.factor(id)29:year_month_id 1.718 ***
## (0.432)
## as.factor(id)30:year_month_id 0.909 *
## (0.348)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 2.227 ***
## (0.571)
## as.factor(id)33:year_month_id -3.136 ***
## (0.200)
## as.factor(id)34:year_month_id -2.746 ***
## (0.382)
## as.factor(id)35:year_month_id 0.940 *
## (0.440)
## as.factor(id)36:year_month_id 0.749 **
## (0.241)
## as.factor(id)37:year_month_id -0.170
## (0.310)
## as.factor(id)38:year_month_id -1.153 ***
## (0.201)
## as.factor(id)39:year_month_id 0.083
## (0.297)
## as.factor(id)40:year_month_id 0.520
## (0.424)
## as.factor(id)41:year_month_id -0.536
## (0.304)
## as.factor(id)42:year_month_id 2.419 ***
## (0.320)
## as.factor(id)43:year_month_id 5.050 ***
## (0.187)
## as.factor(id)44:year_month_id 1.826 ***
## (0.249)
## as.factor(id)45:year_month_id 1.670 ***
## (0.133)
## as.factor(id)46:year_month_id 0.793 ***
## (0.107)
## as.factor(id)47:year_month_id 1.514 *
## (0.581)
## --------------------------------------------------------------------
## R^2 0.903
## Adj. R^2 0.887
## Num. obs. 1551
## RMSE 688.966
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_total_WLS_trend")
# Event study graph
graph_yoy_total_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_total_WLS_trend")
ggplotly(graph_yoy_total_WLS_trend_covar)5.3 WLS, with trends, Figure 4 (c) & TableC.3 (3)
- Y=UI benefit(female), without covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_female,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.392
## (2.270)
## treat_var:date_2018_03 -1.206
## (3.592)
## treat_var:date_2018_04 -1.267
## (4.625)
## treat_var:date_2018_05 -1.006
## (5.354)
## treat_var:date_2018_06 4.252
## (5.426)
## treat_var:date_2018_07 -0.929
## (5.696)
## treat_var:date_2018_08 -7.182
## (7.442)
## treat_var:date_2018_09 -10.305
## (9.067)
## treat_var:date_2018_10 -12.341
## (9.987)
## treat_var:date_2018_11 -6.324
## (7.696)
## treat_var:date_2018_12 -11.792
## (8.732)
## treat_var:date_2019_01 -8.594
## (7.873)
## treat_var:date_2019_02 -3.178
## (7.031)
## treat_var:date_2019_03 -3.569
## (7.803)
## treat_var:date_2019_04 -9.973
## (9.638)
## treat_var:date_2019_05 -3.024
## (7.302)
## treat_var:date_2019_06 -15.497
## (9.845)
## treat_var:date_2019_07 -10.402
## (9.702)
## treat_var:date_2019_08 0.531
## (5.781)
## treat_var:date_2019_09 -0.360
## (6.347)
## treat_var:date_2019_10 0.868
## (5.705)
## treat_var:date_2019_11 -6.418
## (3.334)
## treat_var:date_2019_12 -0.828
## (2.553)
## treat_var:date_2020_02 -3.191
## (4.072)
## treat_var:date_2020_03 0.495
## (5.572)
## treat_var:date_2020_04 5.495
## (6.668)
## treat_var:date_2020_05 5.251
## (7.316)
## treat_var:date_2020_06 10.170
## (12.491)
## treat_var:date_2020_07 16.939
## (12.911)
## treat_var:date_2020_08 11.282
## (15.032)
## treat_var:date_2020_09 17.222
## (14.673)
## as.factor(id)1:year_month_id -0.818
## (0.549)
## as.factor(id)2:year_month_id -0.161
## (0.323)
## as.factor(id)3:year_month_id -0.704
## (0.364)
## as.factor(id)4:year_month_id 1.856 ***
## (0.429)
## as.factor(id)5:year_month_id 0.137
## (0.414)
## as.factor(id)6:year_month_id 0.255
## (0.393)
## as.factor(id)7:year_month_id 1.935 ***
## (0.372)
## as.factor(id)8:year_month_id 1.332 ***
## (0.229)
## as.factor(id)9:year_month_id 2.952 ***
## (0.241)
## as.factor(id)10:year_month_id 4.236 ***
## (0.175)
## as.factor(id)11:year_month_id 0.842
## (0.502)
## as.factor(id)12:year_month_id 0.513
## (0.481)
## as.factor(id)13:year_month_id 1.186 *
## (0.528)
## as.factor(id)14:year_month_id 0.341
## (0.665)
## as.factor(id)15:year_month_id 0.884 **
## (0.301)
## as.factor(id)16:year_month_id 1.308 ***
## (0.331)
## as.factor(id)17:year_month_id 0.706 *
## (0.269)
## as.factor(id)18:year_month_id -0.543 **
## (0.190)
## as.factor(id)19:year_month_id 2.196 ***
## (0.289)
## as.factor(id)20:year_month_id 2.641 ***
## (0.184)
## as.factor(id)21:year_month_id 0.049
## (0.210)
## as.factor(id)22:year_month_id 1.323 ***
## (0.251)
## as.factor(id)23:year_month_id 1.156 ***
## (0.282)
## as.factor(id)24:year_month_id 0.330
## (0.288)
## as.factor(id)25:year_month_id 0.812 **
## (0.264)
## as.factor(id)26:year_month_id -0.432
## (0.444)
## as.factor(id)27:year_month_id 0.832
## (0.643)
## as.factor(id)28:year_month_id -1.486 **
## (0.503)
## as.factor(id)29:year_month_id -0.124
## (0.579)
## as.factor(id)30:year_month_id -0.650
## (0.570)
## as.factor(id)31:year_month_id -2.111 ***
## (0.378)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -4.648 ***
## (0.270)
## as.factor(id)34:year_month_id -4.152 ***
## (0.292)
## as.factor(id)35:year_month_id -0.878 **
## (0.323)
## as.factor(id)36:year_month_id -0.887 *
## (0.361)
## as.factor(id)37:year_month_id -1.009 **
## (0.340)
## as.factor(id)38:year_month_id -2.678 ***
## (0.316)
## as.factor(id)39:year_month_id -1.112 ***
## (0.263)
## as.factor(id)40:year_month_id -0.658
## (0.374)
## as.factor(id)41:year_month_id -2.494 ***
## (0.074)
## as.factor(id)42:year_month_id -0.211
## (0.265)
## as.factor(id)43:year_month_id 4.418 ***
## (0.266)
## as.factor(id)44:year_month_id 0.198
## (0.386)
## as.factor(id)45:year_month_id -0.175
## (0.270)
## as.factor(id)46:year_month_id -1.373 **
## (0.432)
## as.factor(id)47:year_month_id 1.241
## (0.695)
## -------------------------------------------
## R^2 0.833
## Adj. R^2 0.815
## Num. obs. 1551
## RMSE 917.518
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_female_WLS_trend")
# Event study graph
graph_yoy_female_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_female_WLS_trend")
ggplotly(graph_yoy_female_WLS_trend)5.4 WLS, with trends, Figure 4 (d) & TableC.4 (3)
- Y=UI benefit(female), with covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_female,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 -0.391
## (2.324)
## treat_var:date_2018_03 -1.203
## (3.678)
## treat_var:date_2018_04 -1.263
## (4.733)
## treat_var:date_2018_05 -1.001
## (5.480)
## treat_var:date_2018_06 4.258
## (5.551)
## treat_var:date_2018_07 -0.921
## (5.825)
## treat_var:date_2018_08 -7.174
## (7.612)
## treat_var:date_2018_09 -10.295
## (9.274)
## treat_var:date_2018_10 -12.330
## (10.217)
## treat_var:date_2018_11 -6.312
## (7.874)
## treat_var:date_2018_12 -11.778
## (8.930)
## treat_var:date_2019_01 -8.582
## (8.051)
## treat_var:date_2019_02 -3.166
## (7.189)
## treat_var:date_2019_03 -3.556
## (7.978)
## treat_var:date_2019_04 -9.960
## (9.860)
## treat_var:date_2019_05 -3.010
## (7.468)
## treat_var:date_2019_06 -15.483
## (10.073)
## treat_var:date_2019_07 -10.388
## (9.927)
## treat_var:date_2019_08 0.546
## (5.912)
## treat_var:date_2019_09 -0.345
## (6.490)
## treat_var:date_2019_10 0.884
## (5.834)
## treat_var:date_2019_11 -6.402
## (3.407)
## treat_var:date_2019_12 -0.811
## (2.612)
## treat_var:date_2020_02 15.750
## (8.298)
## treat_var:date_2020_03 24.565
## (12.653)
## treat_var:date_2020_04 22.536
## (14.864)
## treat_var:date_2020_05 37.184 **
## (12.692)
## treat_var:date_2020_06 24.375
## (17.225)
## treat_var:date_2020_07 31.002 *
## (15.199)
## treat_var:date_2020_08 15.263
## (13.875)
## treat_var:date_2020_09 19.458
## (13.172)
## date_2020_02:google_mobility_index_2020may -2.170 *
## (1.068)
## date_2020_03:google_mobility_index_2020may -2.606
## (1.313)
## date_2020_04:google_mobility_index_2020may -4.449 *
## (2.021)
## date_2020_05:google_mobility_index_2020may -5.232 **
## (1.801)
## date_2020_06:google_mobility_index_2020may -7.949 **
## (2.464)
## date_2020_07:google_mobility_index_2020may -9.369 ***
## (2.664)
## date_2020_08:google_mobility_index_2020may -8.330 ***
## (1.991)
## date_2020_09:google_mobility_index_2020may -5.943 **
## (2.105)
## date_2020_02:infection_rate_cumulative2020jun 1.607
## (0.909)
## date_2020_03:infection_rate_cumulative2020jun 2.097
## (1.291)
## date_2020_04:infection_rate_cumulative2020jun 0.780
## (1.537)
## date_2020_05:infection_rate_cumulative2020jun -0.436
## (1.418)
## date_2020_06:infection_rate_cumulative2020jun 1.757
## (2.489)
## date_2020_07:infection_rate_cumulative2020jun 0.903
## (2.269)
## date_2020_08:infection_rate_cumulative2020jun 2.247
## (1.998)
## date_2020_09:infection_rate_cumulative2020jun 2.923
## (1.744)
## date_2020_02:death_rate_cumulative2020jun -23.040 *
## (10.471)
## date_2020_03:death_rate_cumulative2020jun -31.443 *
## (14.895)
## date_2020_04:death_rate_cumulative2020jun -21.696
## (18.020)
## date_2020_05:death_rate_cumulative2020jun -6.985
## (15.977)
## date_2020_06:death_rate_cumulative2020jun -30.643
## (31.617)
## date_2020_07:death_rate_cumulative2020jun -21.676
## (28.291)
## date_2020_08:death_rate_cumulative2020jun -28.387
## (23.037)
## date_2020_09:death_rate_cumulative2020jun -33.803
## (17.656)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.007 ***
## (0.002)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.009 **
## (0.003)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.009 **
## (0.003)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.010 ***
## (0.003)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.012 **
## (0.005)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.014 **
## (0.004)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.014 ***
## (0.003)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.011 **
## (0.003)
## date_2020_02:Secondary_industry_ratio 191.942
## (103.221)
## date_2020_03:Secondary_industry_ratio 160.481
## (141.046)
## date_2020_04:Secondary_industry_ratio 135.753
## (196.065)
## date_2020_05:Secondary_industry_ratio 52.320
## (169.256)
## date_2020_06:Secondary_industry_ratio -100.098
## (222.331)
## date_2020_07:Secondary_industry_ratio 41.467
## (213.754)
## date_2020_08:Secondary_industry_ratio 269.216
## (196.400)
## date_2020_09:Secondary_industry_ratio 426.244 *
## (203.459)
## date_2020_02:Tertiary_industry_ratio -111.635
## (129.923)
## date_2020_03:Tertiary_industry_ratio -174.443
## (203.407)
## date_2020_04:Tertiary_industry_ratio -200.714
## (281.262)
## date_2020_05:Tertiary_industry_ratio -505.232 *
## (212.248)
## date_2020_06:Tertiary_industry_ratio -646.027
## (324.688)
## date_2020_07:Tertiary_industry_ratio -640.161 *
## (306.539)
## date_2020_08:Tertiary_industry_ratio -286.029
## (230.120)
## date_2020_09:Tertiary_industry_ratio -66.508
## (205.235)
## date_2020_02:Total_population 0.035 **
## (0.010)
## date_2020_03:Total_population 0.035 *
## (0.015)
## date_2020_04:Total_population 0.050 *
## (0.024)
## date_2020_05:Total_population 0.054 **
## (0.019)
## date_2020_06:Total_population 0.030
## (0.033)
## date_2020_07:Total_population 0.039
## (0.030)
## date_2020_08:Total_population 0.023
## (0.029)
## date_2020_09:Total_population 0.019
## (0.028)
## date_2020_02:Ratio_of_aged_population 0.786
## (0.546)
## date_2020_03:Ratio_of_aged_population 0.575
## (0.613)
## date_2020_04:Ratio_of_aged_population 0.937
## (1.043)
## date_2020_05:Ratio_of_aged_population 0.942
## (0.857)
## date_2020_06:Ratio_of_aged_population 0.318
## (1.358)
## date_2020_07:Ratio_of_aged_population -0.336
## (1.454)
## date_2020_08:Ratio_of_aged_population -1.152
## (1.248)
## date_2020_09:Ratio_of_aged_population -1.297
## (1.211)
## as.factor(id)1:year_month_id -0.010
## (0.536)
## as.factor(id)2:year_month_id 2.289 ***
## (0.211)
## as.factor(id)3:year_month_id 1.028 ***
## (0.189)
## as.factor(id)4:year_month_id 2.534 ***
## (0.376)
## as.factor(id)5:year_month_id 2.377 ***
## (0.366)
## as.factor(id)6:year_month_id 0.648
## (0.555)
## as.factor(id)7:year_month_id 1.858 ***
## (0.464)
## as.factor(id)8:year_month_id 1.185 ***
## (0.328)
## as.factor(id)9:year_month_id 2.400 ***
## (0.370)
## as.factor(id)10:year_month_id 4.567 ***
## (0.511)
## as.factor(id)11:year_month_id 0.724
## (0.375)
## as.factor(id)12:year_month_id 0.289
## (0.472)
## as.factor(id)13:year_month_id 0.960 *
## (0.417)
## as.factor(id)14:year_month_id 0.902 *
## (0.363)
## as.factor(id)15:year_month_id 1.597 ***
## (0.400)
## as.factor(id)16:year_month_id 1.932 **
## (0.704)
## as.factor(id)17:year_month_id 1.356 *
## (0.651)
## as.factor(id)18:year_month_id 0.672
## (0.527)
## as.factor(id)19:year_month_id 1.707 **
## (0.505)
## as.factor(id)20:year_month_id 2.189 ***
## (0.476)
## as.factor(id)21:year_month_id -0.206
## (0.506)
## as.factor(id)22:year_month_id 0.768
## (0.467)
## as.factor(id)23:year_month_id 0.389
## (0.373)
## as.factor(id)24:year_month_id 0.147
## (0.397)
## as.factor(id)25:year_month_id 0.253
## (0.526)
## as.factor(id)26:year_month_id -0.572
## (0.383)
## as.factor(id)27:year_month_id 1.307 **
## (0.418)
## as.factor(id)28:year_month_id -1.204 *
## (0.453)
## as.factor(id)29:year_month_id 1.195 *
## (0.499)
## as.factor(id)30:year_month_id 0.555
## (0.376)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 2.102 ***
## (0.577)
## as.factor(id)33:year_month_id -3.909 ***
## (0.233)
## as.factor(id)34:year_month_id -3.775 ***
## (0.438)
## as.factor(id)35:year_month_id 0.514
## (0.467)
## as.factor(id)36:year_month_id 0.466
## (0.288)
## as.factor(id)37:year_month_id -0.211
## (0.354)
## as.factor(id)38:year_month_id -1.370 ***
## (0.221)
## as.factor(id)39:year_month_id 0.807 *
## (0.300)
## as.factor(id)40:year_month_id -0.242
## (0.484)
## as.factor(id)41:year_month_id -1.271 ***
## (0.332)
## as.factor(id)42:year_month_id 2.149 ***
## (0.314)
## as.factor(id)43:year_month_id 6.117 ***
## (0.194)
## as.factor(id)44:year_month_id 1.576 ***
## (0.260)
## as.factor(id)45:year_month_id 1.909 ***
## (0.133)
## as.factor(id)46:year_month_id 0.672 ***
## (0.127)
## as.factor(id)47:year_month_id 1.221 *
## (0.501)
## --------------------------------------------------------------------
## R^2 0.872
## Adj. R^2 0.851
## Num. obs. 1551
## RMSE 822.623
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_female_WLS_trend")
# Event study graph
graph_yoy_female_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_female_WLS_trend")
ggplotly(graph_yoy_female_WLS_trend_covar)5.5 WLS, with trends, Figure4 (e) & TableC.3 (5)
- Y=UI benefit(male), without covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_male,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.033
## (1.284)
## treat_var:date_2018_03 1.696
## (2.241)
## treat_var:date_2018_04 1.495
## (2.840)
## treat_var:date_2018_05 0.815
## (3.667)
## treat_var:date_2018_06 1.158
## (3.556)
## treat_var:date_2018_07 -0.667
## (4.245)
## treat_var:date_2018_08 1.284
## (4.989)
## treat_var:date_2018_09 -0.173
## (5.738)
## treat_var:date_2018_10 -3.645
## (6.449)
## treat_var:date_2018_11 -3.860
## (6.119)
## treat_var:date_2018_12 -8.723
## (7.223)
## treat_var:date_2019_01 -8.079
## (6.741)
## treat_var:date_2019_02 -3.562
## (6.298)
## treat_var:date_2019_03 -5.566
## (6.806)
## treat_var:date_2019_04 -9.530
## (7.813)
## treat_var:date_2019_05 1.558
## (7.501)
## treat_var:date_2019_06 -6.882
## (8.166)
## treat_var:date_2019_07 -4.932
## (7.760)
## treat_var:date_2019_08 -1.948
## (5.639)
## treat_var:date_2019_09 -0.153
## (5.789)
## treat_var:date_2019_10 -1.926
## (5.816)
## treat_var:date_2019_11 -6.313
## (3.261)
## treat_var:date_2019_12 -3.508
## (2.213)
## treat_var:date_2020_02 -4.260
## (3.956)
## treat_var:date_2020_03 0.005
## (6.193)
## treat_var:date_2020_04 3.232
## (7.446)
## treat_var:date_2020_05 -1.905
## (11.813)
## treat_var:date_2020_06 6.650
## (14.739)
## treat_var:date_2020_07 4.952
## (13.260)
## treat_var:date_2020_08 -0.780
## (16.743)
## treat_var:date_2020_09 1.384
## (17.404)
## as.factor(id)1:year_month_id -0.195
## (0.553)
## as.factor(id)2:year_month_id 0.662 *
## (0.325)
## as.factor(id)3:year_month_id -0.035
## (0.367)
## as.factor(id)4:year_month_id 1.631 ***
## (0.432)
## as.factor(id)5:year_month_id 0.655
## (0.417)
## as.factor(id)6:year_month_id 1.260 **
## (0.395)
## as.factor(id)7:year_month_id 2.975 ***
## (0.374)
## as.factor(id)8:year_month_id 1.753 ***
## (0.231)
## as.factor(id)9:year_month_id 2.189 ***
## (0.243)
## as.factor(id)10:year_month_id 3.549 ***
## (0.177)
## as.factor(id)11:year_month_id 1.746 **
## (0.506)
## as.factor(id)12:year_month_id 0.886
## (0.485)
## as.factor(id)13:year_month_id 1.081 *
## (0.532)
## as.factor(id)14:year_month_id 0.362
## (0.670)
## as.factor(id)15:year_month_id -0.275
## (0.303)
## as.factor(id)16:year_month_id 0.509
## (0.334)
## as.factor(id)17:year_month_id 1.280 ***
## (0.271)
## as.factor(id)18:year_month_id -0.044
## (0.191)
## as.factor(id)19:year_month_id 2.041 ***
## (0.291)
## as.factor(id)20:year_month_id 2.346 ***
## (0.186)
## as.factor(id)21:year_month_id 0.425
## (0.211)
## as.factor(id)22:year_month_id 1.799 ***
## (0.253)
## as.factor(id)23:year_month_id 0.905 **
## (0.285)
## as.factor(id)24:year_month_id 0.486
## (0.290)
## as.factor(id)25:year_month_id 2.229 ***
## (0.266)
## as.factor(id)26:year_month_id -0.392
## (0.447)
## as.factor(id)27:year_month_id 0.868
## (0.648)
## as.factor(id)28:year_month_id -0.297
## (0.507)
## as.factor(id)29:year_month_id 0.608
## (0.583)
## as.factor(id)30:year_month_id 0.420
## (0.574)
## as.factor(id)31:year_month_id -1.540 ***
## (0.381)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -3.482 ***
## (0.272)
## as.factor(id)34:year_month_id -2.960 ***
## (0.294)
## as.factor(id)35:year_month_id -0.503
## (0.326)
## as.factor(id)36:year_month_id -0.459
## (0.364)
## as.factor(id)37:year_month_id -1.591 ***
## (0.343)
## as.factor(id)38:year_month_id -2.028 ***
## (0.318)
## as.factor(id)39:year_month_id -2.403 ***
## (0.265)
## as.factor(id)40:year_month_id 0.269
## (0.377)
## as.factor(id)41:year_month_id -1.030 ***
## (0.075)
## as.factor(id)42:year_month_id 0.443
## (0.267)
## as.factor(id)43:year_month_id 2.270 ***
## (0.269)
## as.factor(id)44:year_month_id 0.910 *
## (0.389)
## as.factor(id)45:year_month_id -0.248
## (0.272)
## as.factor(id)46:year_month_id -0.858
## (0.436)
## as.factor(id)47:year_month_id 1.231
## (0.701)
## -------------------------------------------
## R^2 0.891
## Adj. R^2 0.879
## Num. obs. 1551
## RMSE 710.195
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_male_WLS_trend")
# Event study graph
graph_yoy_male_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_male_WLS_trend")
ggplotly(graph_yoy_male_WLS_trend)5.6 WLS, with trends, Figure4 (f) & TableC.4 (5)
- Y=UI benefit(male), with covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_unemp_benefit_number_male,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.034
## (1.314)
## treat_var:date_2018_03 1.699
## (2.294)
## treat_var:date_2018_04 1.500
## (2.907)
## treat_var:date_2018_05 0.820
## (3.754)
## treat_var:date_2018_06 1.165
## (3.638)
## treat_var:date_2018_07 -0.659
## (4.342)
## treat_var:date_2018_08 1.294
## (5.105)
## treat_var:date_2018_09 -0.162
## (5.869)
## treat_var:date_2018_10 -3.633
## (6.598)
## treat_var:date_2018_11 -3.846
## (6.262)
## treat_var:date_2018_12 -8.708
## (7.389)
## treat_var:date_2019_01 -8.066
## (6.897)
## treat_var:date_2019_02 -3.548
## (6.444)
## treat_var:date_2019_03 -5.552
## (6.966)
## treat_var:date_2019_04 -9.515
## (7.997)
## treat_var:date_2019_05 1.573
## (7.679)
## treat_var:date_2019_06 -6.867
## (8.360)
## treat_var:date_2019_07 -4.916
## (7.943)
## treat_var:date_2019_08 -1.931
## (5.770)
## treat_var:date_2019_09 -0.136
## (5.924)
## treat_var:date_2019_10 -1.909
## (5.951)
## treat_var:date_2019_11 -6.295
## (3.341)
## treat_var:date_2019_12 -3.490
## (2.269)
## treat_var:date_2020_02 9.963
## (9.593)
## treat_var:date_2020_03 18.745
## (12.580)
## treat_var:date_2020_04 18.276
## (14.479)
## treat_var:date_2020_05 28.816
## (15.118)
## treat_var:date_2020_06 24.155
## (16.821)
## treat_var:date_2020_07 24.448
## (18.289)
## treat_var:date_2020_08 23.505
## (21.500)
## treat_var:date_2020_09 24.343
## (21.389)
## date_2020_02:google_mobility_index_2020may 1.122
## (1.239)
## date_2020_03:google_mobility_index_2020may 2.197
## (1.441)
## date_2020_04:google_mobility_index_2020may 0.945
## (1.588)
## date_2020_05:google_mobility_index_2020may 0.987
## (1.426)
## date_2020_06:google_mobility_index_2020may -0.779
## (1.752)
## date_2020_07:google_mobility_index_2020may -3.162
## (2.300)
## date_2020_08:google_mobility_index_2020may -2.222
## (2.511)
## date_2020_09:google_mobility_index_2020may -0.704
## (2.387)
## date_2020_02:infection_rate_cumulative2020jun 1.132
## (1.254)
## date_2020_03:infection_rate_cumulative2020jun 2.081
## (1.565)
## date_2020_04:infection_rate_cumulative2020jun 1.456
## (1.602)
## date_2020_05:infection_rate_cumulative2020jun 2.014
## (1.720)
## date_2020_06:infection_rate_cumulative2020jun 3.713
## (2.143)
## date_2020_07:infection_rate_cumulative2020jun 1.645
## (2.153)
## date_2020_08:infection_rate_cumulative2020jun 2.715
## (2.364)
## date_2020_09:infection_rate_cumulative2020jun 3.124
## (2.195)
## date_2020_02:death_rate_cumulative2020jun -1.415
## (15.813)
## date_2020_03:death_rate_cumulative2020jun -12.391
## (19.631)
## date_2020_04:death_rate_cumulative2020jun -4.208
## (19.309)
## date_2020_05:death_rate_cumulative2020jun -6.399
## (19.168)
## date_2020_06:death_rate_cumulative2020jun -26.606
## (25.359)
## date_2020_07:death_rate_cumulative2020jun -6.334
## (23.578)
## date_2020_08:death_rate_cumulative2020jun -19.109
## (24.312)
## date_2020_09:death_rate_cumulative2020jun -24.422
## (22.571)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.003
## (0.002)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area -0.005
## (0.003)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.006 *
## (0.003)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.010 **
## (0.003)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area -0.011 **
## (0.003)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area -0.010 **
## (0.004)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area -0.010 *
## (0.005)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area -0.008
## (0.005)
## date_2020_02:Secondary_industry_ratio 14.561
## (119.192)
## date_2020_03:Secondary_industry_ratio -64.432
## (140.103)
## date_2020_04:Secondary_industry_ratio -80.444
## (159.203)
## date_2020_05:Secondary_industry_ratio -90.083
## (160.339)
## date_2020_06:Secondary_industry_ratio -220.924
## (160.982)
## date_2020_07:Secondary_industry_ratio -66.395
## (188.582)
## date_2020_08:Secondary_industry_ratio -39.052
## (206.862)
## date_2020_09:Secondary_industry_ratio -59.457
## (219.101)
## date_2020_02:Tertiary_industry_ratio -219.224
## (155.273)
## date_2020_03:Tertiary_industry_ratio -323.486
## (192.242)
## date_2020_04:Tertiary_industry_ratio -347.254
## (227.632)
## date_2020_05:Tertiary_industry_ratio -534.004 **
## (189.444)
## date_2020_06:Tertiary_industry_ratio -673.222 **
## (236.882)
## date_2020_07:Tertiary_industry_ratio -656.626 *
## (247.680)
## date_2020_08:Tertiary_industry_ratio -610.284 *
## (272.762)
## date_2020_09:Tertiary_industry_ratio -580.155 *
## (257.960)
## date_2020_02:Total_population 0.002
## (0.014)
## date_2020_03:Total_population 0.017
## (0.016)
## date_2020_04:Total_population 0.024
## (0.019)
## date_2020_05:Total_population 0.039
## (0.021)
## date_2020_06:Total_population 0.029
## (0.022)
## date_2020_07:Total_population 0.025
## (0.027)
## date_2020_08:Total_population 0.010
## (0.035)
## date_2020_09:Total_population -0.004
## (0.039)
## date_2020_02:Ratio_of_aged_population -0.580
## (0.582)
## date_2020_03:Ratio_of_aged_population -0.970
## (0.693)
## date_2020_04:Ratio_of_aged_population -0.644
## (0.896)
## date_2020_05:Ratio_of_aged_population -0.267
## (0.900)
## date_2020_06:Ratio_of_aged_population -0.727
## (0.966)
## date_2020_07:Ratio_of_aged_population -0.899
## (1.282)
## date_2020_08:Ratio_of_aged_population -1.438
## (1.583)
## date_2020_09:Ratio_of_aged_population -2.010
## (1.603)
## as.factor(id)1:year_month_id 0.027
## (0.419)
## as.factor(id)2:year_month_id 1.873 ***
## (0.216)
## as.factor(id)3:year_month_id 0.956 ***
## (0.231)
## as.factor(id)4:year_month_id 2.963 ***
## (0.318)
## as.factor(id)5:year_month_id 2.098 ***
## (0.456)
## as.factor(id)6:year_month_id 1.339
## (0.677)
## as.factor(id)7:year_month_id 3.037 ***
## (0.547)
## as.factor(id)8:year_month_id 2.173 ***
## (0.331)
## as.factor(id)9:year_month_id 2.326 ***
## (0.414)
## as.factor(id)10:year_month_id 4.323 ***
## (0.454)
## as.factor(id)11:year_month_id 2.119 ***
## (0.356)
## as.factor(id)12:year_month_id 1.710 ***
## (0.411)
## as.factor(id)13:year_month_id 1.410 ***
## (0.319)
## as.factor(id)14:year_month_id 1.761 ***
## (0.388)
## as.factor(id)15:year_month_id 0.670
## (0.427)
## as.factor(id)16:year_month_id 0.814
## (0.621)
## as.factor(id)17:year_month_id 1.702 **
## (0.567)
## as.factor(id)18:year_month_id 0.726
## (0.486)
## as.factor(id)19:year_month_id 2.428 ***
## (0.528)
## as.factor(id)20:year_month_id 2.838 ***
## (0.460)
## as.factor(id)21:year_month_id 1.022 *
## (0.481)
## as.factor(id)22:year_month_id 2.447 ***
## (0.453)
## as.factor(id)23:year_month_id 1.086 *
## (0.442)
## as.factor(id)24:year_month_id 1.192 **
## (0.379)
## as.factor(id)25:year_month_id 2.308 ***
## (0.532)
## as.factor(id)26:year_month_id 0.243
## (0.397)
## as.factor(id)27:year_month_id 1.692 ***
## (0.406)
## as.factor(id)28:year_month_id 0.571
## (0.398)
## as.factor(id)29:year_month_id 2.228 ***
## (0.427)
## as.factor(id)30:year_month_id 1.285 **
## (0.393)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 2.319 ***
## (0.645)
## as.factor(id)33:year_month_id -2.318 ***
## (0.219)
## as.factor(id)34:year_month_id -1.670 ***
## (0.387)
## as.factor(id)35:year_month_id 1.358 **
## (0.481)
## as.factor(id)36:year_month_id 1.016 ***
## (0.258)
## as.factor(id)37:year_month_id -0.144
## (0.327)
## as.factor(id)38:year_month_id -0.935 ***
## (0.222)
## as.factor(id)39:year_month_id -0.826 *
## (0.356)
## as.factor(id)40:year_month_id 1.328 **
## (0.422)
## as.factor(id)41:year_month_id 0.245
## (0.335)
## as.factor(id)42:year_month_id 2.712 ***
## (0.376)
## as.factor(id)43:year_month_id 3.858 ***
## (0.229)
## as.factor(id)44:year_month_id 2.088 ***
## (0.275)
## as.factor(id)45:year_month_id 1.347 ***
## (0.156)
## as.factor(id)46:year_month_id 0.901 ***
## (0.116)
## as.factor(id)47:year_month_id 1.787 *
## (0.752)
## --------------------------------------------------------------------
## R^2 0.910
## Adj. R^2 0.895
## Num. obs. 1551
## RMSE 661.099
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_male_WLS_trend")
# Event study graph
graph_yoy_male_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_male_WLS_trend")
ggplotly(graph_yoy_male_WLS_trend_covar)